PIB
3139561.491
1578335.332
1377525.883
1216771.296
802757.913
706015.9
Prueba de gráfica Animada
Prueba de gráfica Animada
---
title: "PIB First Dashboard Test"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)
library(tidyverse)
library(tsibble)
library(feasts)
library(readxl)
#library(tidyr)
library(lubridate)
library(gganimate)
library(gifski)
library(av)
library(gapminder)
library(stringr)
```
```{r}
#PIB <- read_excel("C:/Users/JoseGallardo/Documents/Ajolotec/PAP/BD_INDICADORES_MACROECONOMICOS.xlsx", sheet = "PIB")
PIB <- read_excel("BD_INDICADORES_MACROECONOMICOS.xlsx", sheet = "PIB")
colnames(PIB) <- c("Clave","Estado","Year","Clave_Sector","Nombre_Sector","Actividad","PIB")
PIB_Test <- PIB %>% as_tsibble(index=Year,key=c(Estado,Nombre_Sector)) %>%
group_by(Estado,Nombre_Sector,Actividad) %>%
summarise(PIB=sum(PIB))
PIB_Anim <- PIB_Test
PIB_Anim$Nombre_Sector <- NULL
PIB_Anim$Actividad <- NULL
PIB_Anim <- PIB_Anim %>% group_by(Year,Estado) %>% summarise(PIB=sum(PIB)) %>%
arrange(Year,desc(PIB)) %>%
mutate(ranking = row_number()) %>% filter(ranking<=15)
```
```{r}
mycolors <- c("blue", "#FFC125", "darkgreen", "darkorange")
```
Interactive Data Visualization
=====================================
Row
-------------------------------------
### Producto Interno Bruto
```{r}
valueBox(paste("PIB"),
color = "warning")
```
### PIB de la Ciudad de México 2019
```{r}
valueBox({t <- PIB_Test %>% group_by(Estado) %>%
summarize(PIB1=sum(PIB)) %>%
filter(Estado=="Ciudad de México"& Year=="2019")
sum(t$PIB1)},
icon = "fa-user")
```
### PIB del Estado de México 2019
```{r}
valueBox({t <- PIB_Test %>% group_by(Estado) %>%
summarize(PIB1=sum(PIB)) %>%
filter(Estado=="Estado de México"& Year=="2019")
sum(t$PIB1)},
icon = "fa-user")
```
### PIB de Nuevo León 2019
```{r}
valueBox({t <- PIB_Test %>% group_by(Estado) %>%
summarize(PIB1=sum(PIB)) %>%
filter(Estado=="Nuevo León"& Year=="2019")
sum(t$PIB1)},
icon = "fa-user")
```
### PIB de Jalisco 2019
```{r}
valueBox({t <- PIB_Test %>% group_by(Estado) %>%
summarize(PIB1=sum(PIB)) %>%
filter(Estado=="Jalisco"& Year=="2019")
sum(t$PIB1)},
icon = "fa-user")
```
### PIB de Veracruz 2019
```{r}
valueBox({t <- PIB_Test %>% group_by(Estado) %>%
summarize(PIB1=sum(PIB)) %>%
filter(Estado=="Veracruz"& Year=="2019")
sum(t$PIB1)},
icon = "fa-user")
```
### PIB de Guanajuato 2019
```{r}
valueBox({t <- PIB_Test %>% group_by(Estado) %>%
summarize(PIB1=sum(PIB)) %>%
filter(Estado=="Guanajuato"& Year=="2019")
sum(t$PIB1)},
icon = "fa-user")
```
Row
-------------------------------
### PIB por estado (más significativos)
```{r}
p1 <- PIB_Test %>% group_by(Estado) %>%
summarize(PIB1=sum(PIB)) %>%
filter(Estado=="Jalisco"|
Estado=="Ciudad de México"|
Estado=="Estado de México"|
Estado=="Nuevo León"|
Estado=="Veracruz"|
Estado=="Guanajuato") %>%
ggplot(aes(x=Year,y=PIB1),size=2) +
geom_line(aes(color=Estado)) +
scale_y_log10() #+
#transition_reveal(PIB1)
p1
```
### PIB estados más significativos
```{r}
p2 <- PIB_Test %>% filter(Year==2018) %>%
group_by(Estado) %>%
summarise(PIB1 = sum(PIB)) %>%
filter(PIB1>700000) %>%
plot_ly(labels = ~Estado,
values = ~PIB1,
marker = list(colors = mycolors)) %>%
add_pie(hole = 0.2) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels=F,
showgrid=F))
p2
```
### PIB por tipo de actividad
```{r}
p3 <- PIB_Test %>% filter(Year==2018) %>%
group_by(Actividad) %>%
summarise(PIB1 = sum(PIB)) %>%
plot_ly(labels = ~Actividad,
values = ~PIB1,
marker = list(colors = mycolors)) %>%
add_pie(hole = 0.2) %>%
layout(xaxis = list(zeroline = F,
showline = F,
showticklabels = F,
showgrid = F),
yaxis = list(zeroline = F,
showline = F,
showticklabels=F,
showgrid=F))
p3
```
PIB
-------------------------------
```{r}
PIB_Table <- PIB_Test %>% group_by(Estado) %>% summarize(Promedio_PIB = mean(PIB,na.rm = TRUE)) %>% top_n(5) %>% arrange(desc(Promedio_PIB))
datatable(PIB_Table,
caption = "Top 5 Estados más significativos",
rownames = T,
options = list(pageLength = 5))
```
```{r}
PIB_Table <- PIB_Test %>% group_by(Estado) %>% summarize(Promedio_PIB = mean(PIB,na.rm = TRUE)) %>% arrange(desc(Promedio_PIB))%>% top_n(-5)
datatable(PIB_Table,
caption = "Top 5 Estados menos significativos",
rownames = T,
options = list(pageLength = 5))
```
Gráfica PIB Animada
=====================================
```{r}
# p4 <- PIB_Test %>% group_by(Estado) %>%
# summarize(PIB1=sum(PIB)) %>%
# ggplot(aes(x=Year,y=PIB1)) +
# geom_line(aes(color=Estado),size=1.3) +
# scale_y_log10()
#
# p4 <- p4 + transition_reveal(PIB1)
#
# anim_save("figs/GIF_TEST.gif",p4)
```

Gráfica PIB Animada
=====================================
```{r}
# animacion <- PIB_Anim %>%
# ggplot() +
# geom_col(aes(ranking, PIB, fill = Estado)) +
# geom_text(aes(ranking, PIB, label = PIB), hjust=-0.1) +
# geom_text(aes(ranking, y=0 , label = PIB), hjust=1.1) +
# geom_text(aes(x=15, y=max(PIB) , label = as.factor(Year)), vjust = 0.2, alpha = 0.5, col = "gray", size = 20) +
# coord_flip(clip = "off", expand = FALSE) + scale_x_reverse() +
# theme_minimal() + theme(
# panel.grid = element_blank(),
# legend.position = "none",
# axis.ticks.y = element_blank(),
# axis.title.y = element_blank(),
# axis.text.y = element_blank(),
# plot.margin = margin(1, 4, 1, 3, "cm")
# ) +
# transition_states(Year, state_length = 0, transition_length = 2) +
# enter_fade() +
# exit_fade() +
# ease_aes('quadratic-in-out')
#
# anim_save("figs/GIF_TEST2.gif",animate(animacion, width = 700, height = 432, fps = 25, duration = 15, rewind = FALSE))
```

Detalles Interno Bruto por INEGI
========================================
```{r}
datatable(PIB_Test,
caption = "Tabla de PIB",
rownames = T,
filter = "top",
options = list(pageLength = 25))
```